EV Charging Station Current Modeling for Erroneous Data Detection
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Solution Overview
Problem
Traditional methods for detecting erroneous data and data spoofing in electric vehicle charging stations are costly and complex, complicating the implementation and increasing the risk of power disruptions from electric utilities.
Innovation Solution
A controller and method that utilize multiple trained data models to predict current at the point of common coupling, ignoring measurements from specific electric vehicle supply equipment, allowing for the detection of erroneous data without additional sensors or equipment, thereby reducing costs and complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods for detecting erroneous data and data spoofing are implemented, then detection capability is improved, but device complexity and cost increase
Solution Approach 1:
The system uses its own existing measurements and data models to detect erroneous data, without requiring external detection equipment. The charging station's controller performs self-diagnosis by comparing predicted current values (generated from data models that ignore specific EVSE measurements) with actual measured current values, allowing it to identify which EVSE is generating erroneous data using only its own resources
Solution Approach 2:
The system creates virtual copies of measurement data by generating multiple predicted current values, each from a data model that excludes measurements from a different EVSE. These predicted values serve as reference copies to compare against the actual measured current, enabling detection of which original measurement is erroneous without needing additional physical sensors
2Measurement precision
If additional sensors or equipment are added for detection, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The system utilizes its own existing current measurement capability and data models to achieve precise detection of erroneous data. The controller already has measurement equipment for monitoring total current at the point of common coupling, and by combining this with data models that systematically exclude individual EVSE measurements, it achieves precise identification of erroneous sources without needing additional detection equipment
Solution Approach 2:
The system replaces physical detection equipment (additional sensors or monitoring devices) with a computational approach using data models and signal processing. Instead of adding mechanical or physical detection hardware, the invention uses software-based data models that mathematically isolate and identify erroneous measurements through comparison of predicted versus actual current values
Data Source
AI summary
In one aspect, a controller for detecting erroneous data generated at an electric vehicle charging station (EVCS) is provided. The EVCS includes a plurality of electric vehicle supply equipment (EVSE) for charging electric vehicles. The controller is configured to store a plurality of data models that predict a current at a point of common coupling (PCC) drawn by the EVCS from a utility, where each of the plurality of data models ignores measurements from a different one of the plurality of EVSEs, generate a plurality of predicted current values, each generated using a different one of plurality of data models, measure an actual current value at the PCC, calculate a plurality of difference values, each comprising a difference between one of the predicted current values and the actual current value, and determine whether the erroneous data is being generated based on the plurality of difference values.